NI, the provider of platform-based systems that enable engineers and scientists to solve the world’s greatest engineering challenges, announced today that SparkCognition is partnering with NI and IBM to collaborate on the Condition Monitoring and Predictive Maintenance Testbed.

The goal of the collaboration is to deliver an unprecedented level of interoperability among operational technology and informational technology as organizations search for better methods to manage and extend the life of aging assets in heavy machinery, power generation, process manufacturing and a variety of other industrial sectors.

In a new age of Big Analog Data™ solutions, users can take advantage of machine learning to harness value from information. They can collect raw data and derive insights to improve operations, equipment and processes. Users can also realize huge cost savings and competitive advantages as artificial intelligence-driven prognostics warn of component failures before they occur, identify suboptimal operating conditions and assist with root-cause analysis.

Stuart Gillen, director of business development at SparkCognition, said:

“With IIoT technologies driving vast sensorization of industrial equipment, and massive amounts of data being collected on those assets, the collaboration between NI and SparkCognition powers the complex and intelligent processing of information to produce valuable insights.”

“We are excited that our platform can acquire the data and extract the features to drive SparkCognition analytics for IIoT solutions,” said Jamie Smith, director of embedded systems at NI. “Combined with existing technologies in the testbed, the addition of SparkCognition presents new ways to help automate the process of turning sensor data into business insight.”

With this software-defined approach, viewing, managing and refining a broad range of assets stands in direct contrast to the traditional, fixed-functionality methods of the past, which often take too much time, rely on hard-to-find talent and require custom model building for each type of asset.